Fetching the paper…
Reading the bibliography…
In this work, we propose FastDPM, a unified framework for fast sampling in diffusion probabilistic models.
DiffWave: A versatile diffusion model for audio synthesis
Z. Kong, W. Ping, J. Huang, K. Zhao, and B. Catanzaro · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2009
Earlier work this paper cites.
Denoising diffusion implicit models
J. Song, C. Meng, and S. Ermon · 2010
Earlier work this paper cites.
CrowdMOS: An approach for crowdsourcing mean opinion score studies
F. Ribeiro, D. Florêncio, C. Zhang, and M. Seltzer · 2011
Earlier work this paper cites.
Score-based generative modeling through stochastic differential equations
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole · 2011
Earlier work this paper cites.
Deep learning face attributes in the wild
Z. Liu, P. Luo, X. Wang, and X. Tang · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
J. Sohl-Dickstein, E. A. Weiss, N. Maheswaranathan, and S. Ganguli · 2015
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2015
Earlier work this paper cites.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
F. Yu, A. Seff, Y. Zhang, S. Song, T. Funkhouser, and J. Xiao · 2015
Earlier work this paper cites.
Improved techniques for training gans
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
Earlier work this paper cites.
Variational walkback: Learning a transition operator as a stochastic recurrent net
A. Goyal, N. R. Ke, S. Ganguli, and Y. Bengio · 2017
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter · 2017
Earlier work this paper cites.
The LJ speech dataset
K. Ito · 2017
Earlier work this paper cites.
Convolutional neural networks for Google speech commands data set with PyTorch , 2017
Y. Xu and E.-O. Tuguldur · 2017
Cited alongside, same era.
Glow: Generative flow with invertible 1x1 convolutions
D. P. Kingma and P. Dhariwal · 2018
Cited alongside, same era.
Speech commands: A dataset for limited-vocabulary speech recognition
P. Warden · 2018
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Y. Song and S. Ermon · 2019
Cited alongside, same era.
WaveGrad: Estimating gradients for waveform generation
N. Chen, Y. Zhang, H. Zen, R. J. Weiss, M. Norouzi, and W. Chan · 2020
Cited alongside, same era.
Argmax flows and multinomial diffusion: Towards non-autoregressive language models
E. Hoogeboom, D. Nielsen, P. Jaini, P. Forré, and M. Welling · 2021
Closest in time.
Diff-tts: A denoising diffusion model for text-to-speech
M. Jeong, H. Kim, S. J. Cheon, B. J. Choi, and N. S. Kim · 2021
Closest in time.
Nu-wave: A diffusion probabilistic model for neural audio upsampling
J. Lee and S. Han · 2021
Closest in time.
Srdiff: Single image super-resolution with diffusion probabilistic models
H. Li, Y. Yang, M. Chang, H. Feng, Z. Xu, Q. Li, and Y. Chen · 2021
Closest in time.
Diffsinger: Diffusion acoustic model for singing voice synthesis
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Pytorch pretrained diffusion models
P. Esser · 2020
Cited alongside, same era.
Learning energy-based models by diffusion recovery likelihood
R. Gao, Y. Song, B. Poole, Y. N. Wu, and D. P. Kingma · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
J. Ho, A. Jain, and P. Abbeel · 2020
Cited alongside, same era.
Analyzing and improving the image quality of stylegan
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila · 2020
Cited alongside, same era.
Fid score for pytorch
S. Lang · 2020
Cited alongside, same era.
WaveFlow: A compact flow-based model for raw audio
W. Ping, K. Peng, K. Zhao, and Z. Song · 2020
Cited alongside, same era.
Improved techniques for training score-based generative models
Y. Song and S. Ermon · 2020
Cited alongside, same era.
J. Liu, C. Li, Y. Ren, F. Chen, P. Liu, and Z. Zhao · 2021
Closest in time.
Diffusion probabilistic models for 3d point cloud generation
S. Luo and W. Hu · 2021
Closest in time.
Improved autoregressive modeling with distribution smoothing
C. Meng, J. Song, Y. Song, S. Zhao, and S. Ermon · 2021
Closest in time.
Symbolic music generation with diffusion models
G. Mittal, J. Engel, C. Hawthorne, and I. Simon · 2021
Closest in time.
Noise level limited sub-modeling for diffusion probabilistic vocoders
T. Okamoto, T. Toda, Y. Shiga, and H. Kawai · 2021
Closest in time.
WaveFlow on SC09 for unconditional generation
W. Ping · 2021
Closest in time.
Grad-tts: A diffusion probabilistic model for text-to-speech
V. Popov, I. Vovk, V. Gogoryan, T. Sadekova, and M. Kudinov · 2021
Closest in time.
Noise estimation for generative diffusion models
R. San-Roman, E. Nachmani, and L. Wolf · 2021
Closest in time.
3d shape generation and completion through point-voxel diffusion
L. Zhou, Y. Du, and J. Wu · 2021
Closest in time.